Solar forecasts are important for low-cost integration of solar energy into the smart grid. Accurate intra-hour predictions of irradiance quantify the variability of solar power at ground level, reduce the uncertainty in power output from solar farm, and are important for real- time grid balancing and management. A multilayered-hybrid- algorithm method is developed to generate real-time intra- hour prediction intervals (PIs) for both global and direct solar irradiance. This forecasting method integrates stochastic learning methods for the prediction of solar irradiation and local sensing techniques for the introduction of exogenous inputs. The research of the proposed forecasting method consists of four objectives : (1) Development of a smart forecasting engine based on advanced stochastic learning methods. (2) Development of an image-based cloud detection system using a cost- competitive fish-eye camera. (3) Integration of the smart forecasting engine with the cloud detection system to create a high-fidelity forecasting model. (4) Development of a hybrid algorithm to provide prediction intervals for the integrated forecasting model. The forecasting method introduced here is deployed in real-time and achieves forecast skills up to 20% over the reference persistence model. Real-time PIs generated from this method achieve coverage probabilities which are consistently higher than the nominal confidence level (90%) regardless of weather condition

The ever-growing installation of solar power systems imposes severe challenges on the operations of local and regional power grids due to the inherent intermittency and variability of ground-level solar irradiance. In recent decades, solar forecasting methodologies for intra-hour, intra-day and day-ahead energy markets have been extensively explored as cost-effective technologies to mitigate the negative effects on the power grids caused by solar power instability. In this work, the progress in intra-hour solar forecasting methodologies are comprehensively reviewed and concisely summarized. The theories behind the forecasting methodologies and how these theories are applied in various forecasting models are presented. The reviewed mathematical tools include regressive methods, stochastic learning methods, deep learning methods, and genetic algorithm. The reviewed forecasting methodologies include data-driven methods, local-sensing methods, hybrid forecasting methods, and application orientated methods that generate probabilistic forecasts and spatial forecasts. Furthermore, suggestions to accelerate the development of future intra-hour forecasting methods are provided.

The dried endosperm of the seed of Phytelephas sp is widely used for artisanal work in the Amazon region due to its favorable mechanical properties and pleasant appearance that resemble elephant ivory. While the seeds have enjoyed popularity and limited use by selected industries (e.g., military uniform buttons and piano keys) and handicraft applications, little is known about the mechanical properties and structure of this sustainable material. This work is the first to characterize the dried Jarina endosperm and to investigate its functionality as a viable substitute for elephant ivory. Structural analysis of typical seeds reveals the prevalence of tubules that align in rings and radiate from the (usually hollow) core of the seed. This seed, in the absence of a reinforcement structure or mineral phase, possesses mechanical properties slightly inferior to elephant ivory and selected plastics, while retaining the visual appeal of a naturally occurring material. A synthetic structure inspired on the seed is created and suggestions for further development are discussed.